Education continues to play an important role in any country’s overall growth. The education market has become more challenging due to the rapid growth and evolution in the modes of imparting education; schools, colleges, private tuition, online education courses, distance education, test preparations, professional trainings etc.The most concerning factor for universities or educational institutions across the world is student dropout rate, especially in developed countries. Here are some facts based on a research about the student drop out patterns in large economies.Challenges

Multiple modes of education

Rapidly changing education trends

Targeting the right population is difficult

Selecting the right students and retaining them (curbing the drop-out rate)

Planning and budgeting for sustainable expansion

Instructor and Curriculum development

Consequences

Lose business gradually due to unawareness about the market and the trend

High drop-out rate

Higher debt pressure on dropouts

Increasing loan defaults

Failure of the education system

Universities lose revenues

How can Analytics help?

Analytics can play a vital role in the education industry by helping universities and institutes make data oriented informed decisions.

Tracking students’ performance across cohort, departments and courses and creating clusters based on different characteristics enables targeted strategies for specific segments of students. such as Students pursuing a particular course and performing exceptionally well or average or below average students finding the course very tough. For the below average cluster, the university administration can initiate structures intervention and provide them some special training to ensure retention and improved performance.

Analyzing the attendance data and focusing on students who missing the assigned course credit can help identify likely dropouts. Specific actions or retention programs for such students can have a significant impact on dropout rates.

Analyzing the trend

Analyze the curriculum and instructor development effectively on a regular basis to keep up with latest trends

Predictive analytics can help predict the future based on the past. It involves digging into historical data, finding key patterns and predicting future trends on the basis of those patterns. Predictive analytics involves various techniques viz. statistics, modelling, machine learning and data mining.

Data mining can help understand the reasons behind a student’s decision to leave the course midway. E.g. Insufficient or no financial-aid, high cost of education, poor grades, choice of subjects, distance from home, good job opportunity or better choice of college etc.